Deep Learning Theory and Applications to the Natural Sciences

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Dr. Pierre Baldi

Director, Institute for Genomics and Bioinformatics (http://www.igb.uci.edu/), UCI
Associate Director, Center for Machine Learning and Data Mining (http://cml.ics.uci.edu/), UCI

Deep learning--essentially learning in complex systems comprised of multiple processing stages--is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics. While we do not yet have a comprehensive theory of deep learning, we will provide a brief overview of a growing body of theoretical results about deep learning highlighting some of the remaining gaps and open questions in the field. We will then present various applications of deep learning to problems in the natural sciences, such as the detection of exotic particles in high-energy physics, the prediction of molecular properties and reactions in chemistry, and the prediction of protein structures in biology.